eomaps 4.2.1

A library to create interactive maps of geographical datasets.

           

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A library to create interactive maps of geographical datasets.

• 🌍 EOmaps provides a simple and intuitive interface to visualize and interact with geographical datasets.
• ⬥ Data can be provided as 1D or 2D lists, numpy-arrays, pandas.DataFrames
•       or directly from GeoTIFFs, NetCDFs and csv-files.
•       ... usable also for large datasets with millions of datapoints!
• ⬥ WebMap layers, annotations, markers can be added with a single line of code
• ⬥ EOmaps is built on top of matplotlib and cartopy and integrates well pandas and geopandas
• 🌎 Quickly turn your maps into powerful interactive data-analysis widgets
• ⬥ use callback functions to interact with the data (or an underlying database)
• ⬥ compare multiple data-layers, WebMaps etc.

❗ update notice: ⚙ Port script from EOmaps v3.x to v4.x

🔨 Installation

To install EOmaps (and all its dependencies) via the conda package-manager, simply use:

conda install -c conda-forge eomaps

... to get a huge speedup, use mamba to solve the dependencies!

conda install -c conda-forge mamba
mamba install -c conda-forge eomaps

For more information, have a look at the installation instructions or checkout the quickstart guide 🚀 from 0 to EOmaps!

📖 Documentation

Make sure to have a look at the 🌳 documentation 🌳 which provides a lot of examples on how to create awesome interactive maps (incl. source code)!

✔️ Citation

Did EOmaps help in your research?
Consider supporting the development and add a citation to your publication!

🚀 Contribute

Found a bug or got an idea for an interesting feature?
Open an issue or start a discussion, and I'll see what I can do!
(I'm of course also happy about actual pull requests on features and bug-fixes!)

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🌳 Basic usage

🛸 Checkout the documentation! 🛸

• A list of coordinates and values is all you need as input!
  • plots of large (>1M datapoints) irregularly sampled datasets are generated in a few seconds!
• Represent your data
  • as shapes with actual geographic dimensions (ellipses, rectangles, geodetic circles)
  • via Voroni diagrams and Delaunay triangulations to get interpolated contour-plots
  • via dynamic data-shading to speed up plots of extremely large datasets
• Re-project the data to any crs supported by cartopy
• Quickly add features and additional layers to the plot
  • Markers, Annotations, WebMap Layers, NaturalEarth features, Scalebars, Compasses (or North-arrows) etc.
• Interact with the data via callback-functions.

from eomaps import Maps

# initialize Maps object
m = Maps(crs=Maps.CRS.Orthographic())

# add basic map-features from NaturalEarth
m.add_feature.preset.coastline()
m.add_feature.cultural_50m.admin_0_countries(fc="none", ec="g")

# easily visualize both structured and unstructured datasets
m.set_data(data=[1, 2, 3, 4], x=[45, 46, 47, 42], y=[23, 24, 25, 26], crs=4326)
# set the shape you want to use to represent the data-points
m.set_shape.geod_circles(radius=10000) # (e.g. geodetic circles with 10km radius)
# (optionally) classify the data
m.set_classify_specs(scheme=Maps.CLASSIFIERS.Quantiles, k=5)
# plot the data
m.plot_map(cmap="viridis", vmin=2, vmax=4)
# add a colorbar with a colored histogram on top
m.add_colorbar(histbins=200)

# add a scalebar
m.add_scalebar()
# add a compass (or north-arrow)
m.add_compass()

# add imagery from a variety of open-access WebMap services
m.add_wms.OpenStreetMap.add_layer.default()

# use callback functions to interact with the map!
m.cb.pick.attach.annotate()

# use multiple layers to compare and analyze different datasets!
m3 = m.new_layer(layer="layer 2")
m3.add_feature.preset.ocean()

# attach a callback to peek on layer 1 if you click on the map
m.cb.click.attach.peek_layer(layer="layer 2", how=0.4)
# attach callbacks to switch between the layers with the keyboard
m.cb.keypress.attach.switch_layer(layer=0, key="0")
m.cb.keypress.attach.switch_layer(layer="layer 2", key="1")

# get a clickable widget to switch between the available plot-layers
m.util.layer_selector()

# add zoomed-in "inset-maps" to highlight areas on th map
m_inset = m.new_inset_map((10, 45))
m_inset.add_feature.preset.coastline(fc="g")

# ---- plot data directly from GeoTIFF / NetCDF or CSV files
m4 = m.new_layer_from_file.GeoTIFF(...)
m4 = m.new_layer_from_file.NetCDF(...)
m4 = m.new_layer_from_file.CSV(...)

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🌼 Thanks to

• Jakob Quast for designing the nice logo!